Overview

Dataset statistics

Number of variables44
Number of observations87980
Missing cells88989
Missing cells (%)2.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.5 MiB
Average record size in memory352.0 B

Variable types

Numeric16
Text10
Categorical14
DateTime3
Unsupported1

Alerts

release_year has constant value "2006"Constant
language_id has constant value "1"Constant
district has constant value " "Constant
language has constant value "English"Constant
actor_id is highly overall correlated with film_actor_idHigh correlation
address_id is highly overall correlated with customer_id and 1 other fieldsHigh correlation
amount is highly overall correlated with rental_rateHigh correlation
category is highly overall correlated with category_idHigh correlation
category_id is highly overall correlated with categoryHigh correlation
customer_id is highly overall correlated with address_id and 1 other fieldsHigh correlation
film_actor_id is highly overall correlated with actor_idHigh correlation
film_category_id is highly overall correlated with film_id and 1 other fieldsHigh correlation
film_id is highly overall correlated with film_category_id and 1 other fieldsHigh correlation
inventory_id is highly overall correlated with film_category_id and 1 other fieldsHigh correlation
manager_staff_id is highly overall correlated with staff_fn and 3 other fieldsHigh correlation
payment_id is highly overall correlated with address_id and 1 other fieldsHigh correlation
rental_rate is highly overall correlated with amountHigh correlation
staff_fn is highly overall correlated with manager_staff_id and 3 other fieldsHigh correlation
staff_id is highly overall correlated with manager_staff_id and 3 other fieldsHigh correlation
staff_ln is highly overall correlated with manager_staff_id and 3 other fieldsHigh correlation
store_id is highly overall correlated with manager_staff_id and 3 other fieldsHigh correlation
active is highly imbalanced (82.7%)Imbalance
return_date has 1009 (1.1%) missing valuesMissing
address2 has 87980 (100.0%) missing valuesMissing
address2 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2026-01-14 06:38:37.102482
Analysis finished2026-01-14 06:38:52.035068
Duration14.93 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

film_id
Real number (ℝ)

High correlation 

Distinct955
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean502.11853
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:52.060387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile48
Q1255
median496
Q3752
95-th percentile952
Maximum1000
Range999
Interquartile range (IQR)497

Descriptive statistics

Standard deviation288.32439
Coefficient of variation (CV)0.57421581
Kurtosis-1.1870348
Mean502.11853
Median Absolute Deviation (MAD)247
Skewness-0.010455299
Sum44176388
Variance83130.956
MonotonicityNot monotonic
2026-01-14T09:38:52.096734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
489288
 
0.3%
892276
 
0.3%
880275
 
0.3%
249273
 
0.3%
649264
 
0.3%
301261
 
0.3%
764252
 
0.3%
418248
 
0.3%
369248
 
0.3%
414240
 
0.3%
Other values (945)85355
97.0%
ValueCountFrequency (%)
1230
0.3%
228
 
< 0.1%
360
 
0.1%
4115
0.1%
560
 
0.1%
6147
0.2%
775
 
0.1%
872
 
0.1%
9108
0.1%
10184
0.2%
ValueCountFrequency (%)
100093
0.1%
99985
0.1%
99854
 
0.1%
99730
 
< 0.1%
99635
 
< 0.1%
99523
 
< 0.1%
99478
0.1%
993180
0.2%
99256
 
0.1%
99164
 
0.1%

title
Text

Distinct955
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:52.208259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length27
Median length22
Mean length14.313969
Min length8

Characters and Unicode

Total characters1259343
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBLANKET BEVERLY
2nd rowBLANKET BEVERLY
3rd rowBLANKET BEVERLY
4th rowBLANKET BEVERLY
5th rowFREAKY POCUS
ValueCountFrequency (%)
heartbreakers659
 
0.4%
boondock655
 
0.4%
armageddon647
 
0.4%
hellfighters642
 
0.4%
apollo625
 
0.4%
polish597
 
0.3%
shakespeare576
 
0.3%
desire568
 
0.3%
instinct558
 
0.3%
love548
 
0.3%
Other values (974)169885
96.5%
2026-01-14T09:38:52.352907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E124731
 
9.9%
A106201
 
8.4%
R91933
 
7.3%
O88834
 
7.1%
87980
 
7.0%
N83346
 
6.6%
I83028
 
6.6%
S78689
 
6.2%
T72852
 
5.8%
L60358
 
4.8%
Other values (17)381391
30.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1171363
93.0%
Space Separator87980
 
7.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E124731
 
10.6%
A106201
 
9.1%
R91933
 
7.8%
O88834
 
7.6%
N83346
 
7.1%
I83028
 
7.1%
S78689
 
6.7%
T72852
 
6.2%
L60358
 
5.2%
C47904
 
4.1%
Other values (16)333487
28.5%
Space Separator
ValueCountFrequency (%)
87980
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1171363
93.0%
Common87980
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E124731
 
10.6%
A106201
 
9.1%
R91933
 
7.8%
O88834
 
7.6%
N83346
 
7.1%
I83028
 
7.1%
S78689
 
6.7%
T72852
 
6.2%
L60358
 
5.2%
C47904
 
4.1%
Other values (16)333487
28.5%
Common
ValueCountFrequency (%)
87980
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1259343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E124731
 
9.9%
A106201
 
8.4%
R91933
 
7.3%
O88834
 
7.1%
87980
 
7.0%
N83346
 
6.6%
I83028
 
6.6%
S78689
 
6.2%
T72852
 
5.8%
L60358
 
4.8%
Other values (17)381391
30.3%
Distinct955
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:52.435871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length130
Median length115
Mean length94.309241
Min length70

Characters and Unicode

Total characters8297327
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA Emotional Documentary of a Student And a Girl who must Build a Boat in Nigeria
2nd rowA Emotional Documentary of a Student And a Girl who must Build a Boat in Nigeria
3rd rowA Emotional Documentary of a Student And a Girl who must Build a Boat in Nigeria
4th rowA Emotional Documentary of a Student And a Girl who must Build a Boat in Nigeria
5th rowA Fast-Paced Documentary of a Pastry Chef And a Crocodile who must Chase a Squirrel in The Gulf of Mexico
ValueCountFrequency (%)
a388545
24.0%
of91030
 
5.6%
and87980
 
5.4%
who87980
 
5.4%
must87980
 
5.4%
in87980
 
5.4%
the16917
 
1.0%
mad15834
 
1.0%
shark10679
 
0.7%
boat10371
 
0.6%
Other values (139)731635
45.2%
2026-01-14T09:38:52.559501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1528951
18.4%
a727326
 
8.8%
n571906
 
6.9%
o508316
 
6.1%
e496071
 
6.0%
t473278
 
5.7%
i420949
 
5.1%
r339771
 
4.1%
s298832
 
3.6%
A286028
 
3.4%
Other values (42)2645899
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5722202
69.0%
Space Separator1528951
 
18.4%
Uppercase Letter1029104
 
12.4%
Dash Punctuation17070
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a727326
12.7%
n571906
10.0%
o508316
 
8.9%
e496071
 
8.7%
t473278
 
8.3%
i420949
 
7.4%
r339771
 
5.9%
s298832
 
5.2%
u272387
 
4.8%
h208522
 
3.6%
Other values (16)1404844
24.6%
Uppercase Letter
ValueCountFrequency (%)
A286028
27.8%
S102821
 
10.0%
C85129
 
8.3%
M69641
 
6.8%
D61483
 
6.0%
B59350
 
5.8%
T57431
 
5.6%
F56860
 
5.5%
P46769
 
4.5%
W32959
 
3.2%
Other values (14)170633
16.6%
Space Separator
ValueCountFrequency (%)
1528951
100.0%
Dash Punctuation
ValueCountFrequency (%)
-17070
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6751306
81.4%
Common1546021
 
18.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a727326
 
10.8%
n571906
 
8.5%
o508316
 
7.5%
e496071
 
7.3%
t473278
 
7.0%
i420949
 
6.2%
r339771
 
5.0%
s298832
 
4.4%
A286028
 
4.2%
u272387
 
4.0%
Other values (40)2356442
34.9%
Common
ValueCountFrequency (%)
1528951
98.9%
-17070
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8297327
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1528951
18.4%
a727326
 
8.8%
n571906
 
6.9%
o508316
 
6.1%
e496071
 
6.0%
t473278
 
5.7%
i420949
 
5.1%
r339771
 
4.1%
s298832
 
3.6%
A286028
 
3.4%
Other values (42)2645899
31.9%

release_year
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
2006
87980 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters351920
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2006
2nd row2006
3rd row2006
4th row2006
5th row2006

Common Values

ValueCountFrequency (%)
200687980
100.0%

Length

2026-01-14T09:38:52.593504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T09:38:52.616475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
200687980
100.0%

Most occurring characters

ValueCountFrequency (%)
0175960
50.0%
287980
25.0%
687980
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number351920
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0175960
50.0%
287980
25.0%
687980
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common351920
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0175960
50.0%
287980
25.0%
687980
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII351920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0175960
50.0%
287980
25.0%
687980
25.0%

language_id
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
1
87980 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters87980
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
187980
100.0%

Length

2026-01-14T09:38:52.638627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T09:38:52.656786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
187980
100.0%

Most occurring characters

ValueCountFrequency (%)
187980
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number87980
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
187980
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common87980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
187980
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII87980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
187980
100.0%

rental_duration
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
6
19279 
4
18643 
3
18208 
5
17551 
7
14299 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters87980
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7
2nd row7
3rd row7
4th row7
5th row7

Common Values

ValueCountFrequency (%)
619279
21.9%
418643
21.2%
318208
20.7%
517551
19.9%
714299
16.3%

Length

2026-01-14T09:38:52.678948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T09:38:52.702303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
619279
21.9%
418643
21.2%
318208
20.7%
517551
19.9%
714299
16.3%

Most occurring characters

ValueCountFrequency (%)
619279
21.9%
418643
21.2%
318208
20.7%
517551
19.9%
714299
16.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number87980
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
619279
21.9%
418643
21.2%
318208
20.7%
517551
19.9%
714299
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common87980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
619279
21.9%
418643
21.2%
318208
20.7%
517551
19.9%
714299
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII87980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
619279
21.9%
418643
21.2%
318208
20.7%
517551
19.9%
714299
16.3%

rental_rate
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
0.99
31130 
2.99
28943 
4.99
27907 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters351920
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.99
2nd row2.99
3rd row2.99
4th row2.99
5th row2.99

Common Values

ValueCountFrequency (%)
0.9931130
35.4%
2.9928943
32.9%
4.9927907
31.7%

Length

2026-01-14T09:38:52.733250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T09:38:52.753845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.9931130
35.4%
2.9928943
32.9%
4.9927907
31.7%

Most occurring characters

ValueCountFrequency (%)
9175960
50.0%
.87980
25.0%
031130
 
8.8%
228943
 
8.2%
427907
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number263940
75.0%
Other Punctuation87980
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9175960
66.7%
031130
 
11.8%
228943
 
11.0%
427907
 
10.6%
Other Punctuation
ValueCountFrequency (%)
.87980
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common351920
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9175960
50.0%
.87980
25.0%
031130
 
8.8%
228943
 
8.2%
427907
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII351920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9175960
50.0%
.87980
25.0%
031130
 
8.8%
228943
 
8.2%
427907
 
7.9%

length
Real number (ℝ)

Distinct140
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.10414
Minimum46
Maximum185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:52.781061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile52
Q180
median114
Q3150
95-th percentile178
Maximum185
Range139
Interquartile range (IQR)70

Descriptive statistics

Standard deviation40.499913
Coefficient of variation (CV)0.35185454
Kurtosis-1.197223
Mean115.10414
Median Absolute Deviation (MAD)35
Skewness0.02742301
Sum10126862
Variance1640.2429
MonotonicityNot monotonic
2026-01-14T09:38:52.818715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
851495
 
1.7%
1121358
 
1.5%
1781355
 
1.5%
841178
 
1.3%
801072
 
1.2%
631070
 
1.2%
751056
 
1.2%
921041
 
1.2%
1791019
 
1.2%
1611008
 
1.1%
Other values (130)76328
86.8%
ValueCountFrequency (%)
46585
0.7%
47732
0.8%
48832
0.9%
49369
0.4%
50563
0.6%
51647
0.7%
52704
0.8%
53759
0.9%
54524
0.6%
55336
0.4%
ValueCountFrequency (%)
185936
1.1%
184393
 
0.4%
183417
 
0.5%
182252
 
0.3%
181837
1.0%
180376
 
0.4%
1791019
1.2%
1781355
1.5%
177660
0.8%
176893
1.0%

replacement_cost
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.190762
Minimum9.99
Maximum29.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:52.850715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9.99
5-th percentile10.99
Q114.99
median20.99
Q324.99
95-th percentile29.99
Maximum29.99
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.0882873
Coefficient of variation (CV)0.30153827
Kurtosis-1.2239788
Mean20.190762
Median Absolute Deviation (MAD)6
Skewness-0.0352181
Sum1776383.2
Variance37.067243
MonotonicityNot monotonic
2026-01-14T09:38:52.880544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
22.995626
 
6.4%
27.995215
 
5.9%
12.995024
 
5.7%
14.994893
 
5.6%
21.994886
 
5.6%
29.994686
 
5.3%
20.994672
 
5.3%
26.994264
 
4.8%
13.994213
 
4.8%
11.994167
 
4.7%
Other values (11)40334
45.8%
ValueCountFrequency (%)
9.993924
4.5%
10.993526
4.0%
11.994167
4.7%
12.995024
5.7%
13.994213
4.8%
14.994893
5.6%
15.993141
3.6%
16.993593
4.1%
17.993748
4.3%
18.993744
4.3%
ValueCountFrequency (%)
29.994686
5.3%
28.994080
4.6%
27.995215
5.9%
26.994264
4.8%
25.993727
4.2%
24.993431
3.9%
23.993870
4.4%
22.995626
6.4%
21.994886
5.6%
20.994672
5.3%

rating
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
PG-13
19065 
PG
18635 
NC-17
17630 
R
17210 
G
15440 

Length

Max length5
Median length2
Mean length2.8801432
Min length1

Characters and Unicode

Total characters253395
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowG
2nd rowG
3rd rowG
4th rowG
5th rowR

Common Values

ValueCountFrequency (%)
PG-1319065
21.7%
PG18635
21.2%
NC-1717630
20.0%
R17210
19.6%
G15440
17.5%

Length

2026-01-14T09:38:52.914626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T09:38:52.938295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
pg-1319065
21.7%
pg18635
21.2%
nc-1717630
20.0%
r17210
19.6%
g15440
17.5%

Most occurring characters

ValueCountFrequency (%)
G53140
21.0%
P37700
14.9%
-36695
14.5%
136695
14.5%
319065
 
7.5%
N17630
 
7.0%
C17630
 
7.0%
717630
 
7.0%
R17210
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter143310
56.6%
Decimal Number73390
29.0%
Dash Punctuation36695
 
14.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G53140
37.1%
P37700
26.3%
N17630
 
12.3%
C17630
 
12.3%
R17210
 
12.0%
Decimal Number
ValueCountFrequency (%)
136695
50.0%
319065
26.0%
717630
24.0%
Dash Punctuation
ValueCountFrequency (%)
-36695
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin143310
56.6%
Common110085
43.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
G53140
37.1%
P37700
26.3%
N17630
 
12.3%
C17630
 
12.3%
R17210
 
12.0%
Common
ValueCountFrequency (%)
-36695
33.3%
136695
33.3%
319065
17.3%
717630
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII253395
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G53140
21.0%
P37700
14.9%
-36695
14.5%
136695
14.5%
319065
 
7.5%
N17630
 
7.0%
C17630
 
7.0%
717630
 
7.0%
R17210
 
6.8%

actor_id
Real number (ℝ)

High correlation 

Distinct200
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.9536
Minimum1
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:52.972479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q152
median102
Q3149
95-th percentile191
Maximum200
Range199
Interquartile range (IQR)97

Descriptive statistics

Standard deviation56.900302
Coefficient of variation (CV)0.56362824
Kurtosis-1.1747542
Mean100.9536
Median Absolute Deviation (MAD)48
Skewness0.0020452118
Sum8881898
Variance3237.6443
MonotonicityNot monotonic
2026-01-14T09:38:53.009896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107753
 
0.9%
181678
 
0.8%
198674
 
0.8%
144654
 
0.7%
102640
 
0.7%
60612
 
0.7%
150611
 
0.7%
37605
 
0.7%
23604
 
0.7%
90599
 
0.7%
Other values (190)81550
92.7%
ValueCountFrequency (%)
1305
0.3%
2387
0.4%
3311
0.4%
4274
0.3%
5496
0.6%
6279
0.3%
7479
0.5%
8317
0.4%
9381
0.4%
10362
0.4%
ValueCountFrequency (%)
200346
0.4%
199255
 
0.3%
198674
0.8%
197553
0.6%
196450
0.5%
195450
0.5%
194380
0.4%
193444
0.5%
192458
0.5%
191523
0.6%
Distinct128
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:53.112869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length8
Mean length5.3013185
Min length2

Characters and Unicode

Total characters466410
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFRED
2nd rowALAN
3rd rowBURT
4th rowTHORA
5th rowTOM
ValueCountFrequency (%)
penelope1661
 
1.9%
kenneth1643
 
1.9%
jayne1565
 
1.8%
matthew1518
 
1.7%
julia1435
 
1.6%
groucho1350
 
1.5%
morgan1340
 
1.5%
ed1310
 
1.5%
burt1304
 
1.5%
christian1286
 
1.5%
Other values (118)73568
83.6%
2026-01-14T09:38:53.243437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E65221
14.0%
A52730
 
11.3%
R41082
 
8.8%
N40749
 
8.7%
L29589
 
6.3%
I29424
 
6.3%
O18594
 
4.0%
S18574
 
4.0%
T18148
 
3.9%
M17206
 
3.7%
Other values (14)135093
29.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter466410
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E65221
14.0%
A52730
 
11.3%
R41082
 
8.8%
N40749
 
8.7%
L29589
 
6.3%
I29424
 
6.3%
O18594
 
4.0%
S18574
 
4.0%
T18148
 
3.9%
M17206
 
3.7%
Other values (14)135093
29.0%

Most occurring scripts

ValueCountFrequency (%)
Latin466410
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E65221
14.0%
A52730
 
11.3%
R41082
 
8.8%
N40749
 
8.7%
L29589
 
6.3%
I29424
 
6.3%
O18594
 
4.0%
S18574
 
4.0%
T18148
 
3.9%
M17206
 
3.7%
Other values (14)135093
29.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII466410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E65221
14.0%
A52730
 
11.3%
R41082
 
8.8%
N40749
 
8.7%
L29589
 
6.3%
I29424
 
6.3%
O18594
 
4.0%
S18574
 
4.0%
T18148
 
3.9%
M17206
 
3.7%
Other values (14)135093
29.0%
Distinct121
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:53.343841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length9
Mean length6.2452489
Min length3

Characters and Unicode

Total characters549457
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCOSTNER
2nd rowDREYFUSS
3rd rowTEMPLE
4th rowTEMPLE
5th rowMIRANDA
ValueCountFrequency (%)
kilmer2145
 
2.4%
nolte2119
 
2.4%
temple1778
 
2.0%
degeneres1611
 
1.8%
keitel1586
 
1.8%
berry1480
 
1.7%
torn1478
 
1.7%
hoffman1455
 
1.7%
guiness1425
 
1.6%
garland1394
 
1.6%
Other values (111)71509
81.3%
2026-01-14T09:38:53.474125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E68603
12.5%
N44874
 
8.2%
R41113
 
7.5%
A39729
 
7.2%
O39689
 
7.2%
L39441
 
7.2%
I33954
 
6.2%
S32294
 
5.9%
T23663
 
4.3%
D21500
 
3.9%
Other values (17)164597
30.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter548971
99.9%
Dash Punctuation486
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E68603
12.5%
N44874
 
8.2%
R41113
 
7.5%
A39729
 
7.2%
O39689
 
7.2%
L39441
 
7.2%
I33954
 
6.2%
S32294
 
5.9%
T23663
 
4.3%
D21500
 
3.9%
Other values (16)164111
29.9%
Dash Punctuation
ValueCountFrequency (%)
-486
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin548971
99.9%
Common486
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E68603
12.5%
N44874
 
8.2%
R41113
 
7.5%
A39729
 
7.2%
O39689
 
7.2%
L39441
 
7.2%
I33954
 
6.2%
S32294
 
5.9%
T23663
 
4.3%
D21500
 
3.9%
Other values (16)164111
29.9%
Common
ValueCountFrequency (%)
-486
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII549457
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E68603
12.5%
N44874
 
8.2%
R41113
 
7.5%
A39729
 
7.2%
O39689
 
7.2%
L39441
 
7.2%
I33954
 
6.2%
S32294
 
5.9%
T23663
 
4.3%
D21500
 
3.9%
Other values (17)164597
30.0%

customer_id
Real number (ℝ)

High correlation 

Distinct599
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean297.18662
Minimum1
Maximum599
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:53.510816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile29
Q1148
median296
Q3446
95-th percentile568
Maximum599
Range598
Interquartile range (IQR)298

Descriptive statistics

Standard deviation172.25283
Coefficient of variation (CV)0.57961164
Kurtosis-1.1882855
Mean297.18662
Median Absolute Deviation (MAD)149
Skewness0.0085538465
Sum26146479
Variance29671.036
MonotonicityNot monotonic
2026-01-14T09:38:53.547332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
148272
 
0.3%
526251
 
0.3%
197244
 
0.3%
144239
 
0.3%
236233
 
0.3%
75233
 
0.3%
29223
 
0.3%
257221
 
0.3%
178220
 
0.3%
410216
 
0.2%
Other values (589)85628
97.3%
ValueCountFrequency (%)
1156
0.2%
2175
0.2%
3144
0.2%
4121
0.1%
5183
0.2%
6171
0.2%
7191
0.2%
8152
0.2%
9129
0.1%
10123
0.1%
ValueCountFrequency (%)
599102
0.1%
598102
0.1%
597144
0.2%
596158
0.2%
595141
0.2%
594141
0.2%
593129
0.1%
592145
0.2%
591120
0.1%
590134
0.2%

cust_fn
Text

Distinct591
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:53.661306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length10
Mean length5.6729484
Min length2

Characters and Unicode

Total characters499106
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHARLOTTE
2nd rowCHARLOTTE
3rd rowCHARLOTTE
4th rowCHARLOTTE
5th rowTOMMY
ValueCountFrequency (%)
marion388
 
0.4%
leslie325
 
0.4%
tracy303
 
0.3%
terry291
 
0.3%
willie291
 
0.3%
jessie290
 
0.3%
jamie277
 
0.3%
eleanor272
 
0.3%
kelly272
 
0.3%
karl251
 
0.3%
Other values (581)85020
96.6%
2026-01-14T09:38:53.804747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E62884
12.6%
A61284
12.3%
R47045
 
9.4%
N42774
 
8.6%
I38750
 
7.8%
L36712
 
7.4%
O23409
 
4.7%
T21484
 
4.3%
S19614
 
3.9%
D18887
 
3.8%
Other values (16)126263
25.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter499106
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E62884
12.6%
A61284
12.3%
R47045
 
9.4%
N42774
 
8.6%
I38750
 
7.8%
L36712
 
7.4%
O23409
 
4.7%
T21484
 
4.3%
S19614
 
3.9%
D18887
 
3.8%
Other values (16)126263
25.3%

Most occurring scripts

ValueCountFrequency (%)
Latin499106
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E62884
12.6%
A61284
12.3%
R47045
 
9.4%
N42774
 
8.6%
I38750
 
7.8%
L36712
 
7.4%
O23409
 
4.7%
T21484
 
4.3%
S19614
 
3.9%
D18887
 
3.8%
Other values (16)126263
25.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII499106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E62884
12.6%
A61284
12.3%
R47045
 
9.4%
N42774
 
8.6%
I38750
 
7.8%
L36712
 
7.4%
O23409
 
4.7%
T21484
 
4.3%
S19614
 
3.9%
D18887
 
3.8%
Other values (16)126263
25.3%

cust_ln
Text

Distinct599
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:53.905724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length10
Mean length6.1921459
Min length2

Characters and Unicode

Total characters544785
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHUNTER
2nd rowHUNTER
3rd rowHUNTER
4th rowHUNTER
5th rowCOLLAZO
ValueCountFrequency (%)
hunt272
 
0.3%
seal251
 
0.3%
peters244
 
0.3%
shaw239
 
0.3%
dean233
 
0.3%
sanders233
 
0.3%
hernandez223
 
0.3%
douglas221
 
0.3%
snyder220
 
0.3%
irby216
 
0.2%
Other values (589)85628
97.3%
2026-01-14T09:38:54.043525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E58265
 
10.7%
R54380
 
10.0%
A47359
 
8.7%
N43534
 
8.0%
O38957
 
7.2%
L38567
 
7.1%
S35961
 
6.6%
I26872
 
4.9%
T26008
 
4.8%
H20076
 
3.7%
Other values (16)154806
28.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter544785
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E58265
 
10.7%
R54380
 
10.0%
A47359
 
8.7%
N43534
 
8.0%
O38957
 
7.2%
L38567
 
7.1%
S35961
 
6.6%
I26872
 
4.9%
T26008
 
4.8%
H20076
 
3.7%
Other values (16)154806
28.4%

Most occurring scripts

ValueCountFrequency (%)
Latin544785
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E58265
 
10.7%
R54380
 
10.0%
A47359
 
8.7%
N43534
 
8.0%
O38957
 
7.2%
L38567
 
7.1%
S35961
 
6.6%
I26872
 
4.9%
T26008
 
4.8%
H20076
 
3.7%
Other values (16)154806
28.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII544785
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E58265
 
10.7%
R54380
 
10.0%
A47359
 
8.7%
N43534
 
8.0%
O38957
 
7.2%
L38567
 
7.1%
S35961
 
6.6%
I26872
 
4.9%
T26008
 
4.8%
H20076
 
3.7%
Other values (16)154806
28.4%

email
Text

Distinct599
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:54.219554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length40
Median length38
Mean length31.865094
Min length26

Characters and Unicode

Total characters2803491
Distinct characters41
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHARLOTTE.HUNTER@sakilacustomer.org
2nd rowCHARLOTTE.HUNTER@sakilacustomer.org
3rd rowCHARLOTTE.HUNTER@sakilacustomer.org
4th rowCHARLOTTE.HUNTER@sakilacustomer.org
5th rowTOMMY.COLLAZO@sakilacustomer.org
ValueCountFrequency (%)
eleanor.hunt@sakilacustomer.org272
 
0.3%
karl.seal@sakilacustomer.org251
 
0.3%
sue.peters@sakilacustomer.org244
 
0.3%
clara.shaw@sakilacustomer.org239
 
0.3%
marcia.dean@sakilacustomer.org233
 
0.3%
tammy.sanders@sakilacustomer.org233
 
0.3%
angela.hernandez@sakilacustomer.org223
 
0.3%
marsha.douglas@sakilacustomer.org221
 
0.3%
marion.snyder@sakilacustomer.org220
 
0.3%
curtis.irby@sakilacustomer.org216
 
0.2%
Other values (589)85628
97.3%
2026-01-14T09:38:54.352916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r175960
 
6.3%
.175960
 
6.3%
o175960
 
6.3%
s175960
 
6.3%
a175960
 
6.3%
E121149
 
4.3%
A108643
 
3.9%
R101425
 
3.6%
l87980
 
3.1%
g87980
 
3.1%
Other values (31)1416514
50.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1495660
53.3%
Uppercase Letter1043891
37.2%
Other Punctuation263940
 
9.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E121149
11.6%
A108643
 
10.4%
R101425
 
9.7%
N86308
 
8.3%
L75279
 
7.2%
I65622
 
6.3%
O62366
 
6.0%
S55575
 
5.3%
T47492
 
4.5%
C36337
 
3.5%
Other values (16)283695
27.2%
Lowercase Letter
ValueCountFrequency (%)
r175960
11.8%
o175960
11.8%
s175960
11.8%
a175960
11.8%
l87980
 
5.9%
g87980
 
5.9%
e87980
 
5.9%
t87980
 
5.9%
u87980
 
5.9%
c87980
 
5.9%
Other values (3)263940
17.6%
Other Punctuation
ValueCountFrequency (%)
.175960
66.7%
@87980
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin2539551
90.6%
Common263940
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
r175960
 
6.9%
o175960
 
6.9%
s175960
 
6.9%
a175960
 
6.9%
E121149
 
4.8%
A108643
 
4.3%
R101425
 
4.0%
l87980
 
3.5%
g87980
 
3.5%
e87980
 
3.5%
Other values (29)1240554
48.8%
Common
ValueCountFrequency (%)
.175960
66.7%
@87980
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2803491
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r175960
 
6.3%
.175960
 
6.3%
o175960
 
6.3%
s175960
 
6.3%
a175960
 
6.3%
E121149
 
4.3%
A108643
 
3.9%
R101425
 
3.6%
l87980
 
3.1%
g87980
 
3.1%
Other values (31)1416514
50.5%

active
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
1
85714 
0
 
2266

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters87980
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
185714
97.4%
02266
 
2.6%

Length

2026-01-14T09:38:54.387350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T09:38:54.406524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
185714
97.4%
02266
 
2.6%

Most occurring characters

ValueCountFrequency (%)
185714
97.4%
02266
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number87980
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
185714
97.4%
02266
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common87980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
185714
97.4%
02266
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII87980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
185714
97.4%
02266
 
2.6%

rental_id
Real number (ℝ)

Distinct16004
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8034.5767
Minimum1
Maximum16049
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:54.431894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile820
Q14030.75
median8041.5
Q312027.25
95-th percentile15247.05
Maximum16049
Range16048
Interquartile range (IQR)7996.5

Descriptive statistics

Standard deviation4630.5566
Coefficient of variation (CV)0.57632863
Kurtosis-1.2005905
Mean8034.5767
Median Absolute Deviation (MAD)4000.5
Skewness-0.0012045241
Sum7.0688206 × 108
Variance21442055
MonotonicityIncreasing
2026-01-14T09:38:54.470231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
484515
 
< 0.1%
522115
 
< 0.1%
744615
 
< 0.1%
1420015
 
< 0.1%
715215
 
< 0.1%
1270115
 
< 0.1%
1518715
 
< 0.1%
279615
 
< 0.1%
593015
 
< 0.1%
801415
 
< 0.1%
Other values (15994)87830
99.8%
ValueCountFrequency (%)
14
< 0.1%
25
< 0.1%
32
 
< 0.1%
44
< 0.1%
58
< 0.1%
64
< 0.1%
76
< 0.1%
84
< 0.1%
93
 
< 0.1%
107
< 0.1%
ValueCountFrequency (%)
160496
< 0.1%
160482
 
< 0.1%
160477
< 0.1%
160465
< 0.1%
160455
< 0.1%
160445
< 0.1%
160437
< 0.1%
160423
 
< 0.1%
160416
< 0.1%
160408
< 0.1%
Distinct15776
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
Minimum2005-05-24 22:53:30
Maximum2006-02-14 15:16:03
Invalid dates0
Invalid dates (%)0.0%
2026-01-14T09:38:54.508701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:54.547500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

return_date
Date

Missing 

Distinct15796
Distinct (%)18.2%
Missing1009
Missing (%)1.1%
Memory size687.5 KiB
Minimum2005-05-25 23:55:21
Maximum2005-09-02 02:35:22
Invalid dates0
Invalid dates (%)0.0%
2026-01-14T09:38:54.585080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:54.623943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

payment_id
Real number (ℝ)

High correlation 

Distinct16004
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8025.7845
Minimum1
Maximum16049
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:54.659773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile796
Q14037
median8032
Q312028
95-th percentile15217
Maximum16049
Range16048
Interquartile range (IQR)7991

Descriptive statistics

Standard deviation4627.488
Coefficient of variation (CV)0.57657765
Kurtosis-1.1977116
Mean8025.7845
Median Absolute Deviation (MAD)3995
Skewness-0.0063754037
Sum7.0610852 × 108
Variance21413645
MonotonicityNot monotonic
2026-01-14T09:38:54.698413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1483915
 
< 0.1%
228015
 
< 0.1%
1312915
 
< 0.1%
945715
 
< 0.1%
1000415
 
< 0.1%
1320215
 
< 0.1%
1246315
 
< 0.1%
566215
 
< 0.1%
685315
 
< 0.1%
28815
 
< 0.1%
Other values (15994)87830
99.8%
ValueCountFrequency (%)
14
< 0.1%
25
< 0.1%
31
 
< 0.1%
43
< 0.1%
51
 
< 0.1%
66
< 0.1%
74
< 0.1%
86
< 0.1%
95
< 0.1%
105
< 0.1%
ValueCountFrequency (%)
160494
< 0.1%
160488
< 0.1%
160475
< 0.1%
160464
< 0.1%
160453
 
< 0.1%
160445
< 0.1%
160433
 
< 0.1%
160425
< 0.1%
160419
< 0.1%
160406
< 0.1%

amount
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1777571
Minimum0
Maximum11.99
Zeros133
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:54.729647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.99
Q12.99
median3.99
Q34.99
95-th percentile8.99
Maximum11.99
Range11.99
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.3455906
Coefficient of variation (CV)0.56144734
Kurtosis-0.24036645
Mean4.1777571
Median Absolute Deviation (MAD)1
Skewness0.46814077
Sum367559.07
Variance5.5017954
MonotonicityNot monotonic
2026-01-14T09:38:54.756352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
4.9920434
23.2%
2.9919873
22.6%
0.9916264
18.5%
5.997181
 
8.2%
3.996350
 
7.2%
6.995929
 
6.7%
7.993654
 
4.2%
1.993490
 
4.0%
8.992633
 
3.0%
9.991302
 
1.5%
Other values (9)870
 
1.0%
ValueCountFrequency (%)
0133
 
0.2%
0.9916264
18.5%
1.984
 
< 0.1%
1.993490
 
4.0%
2.9919873
22.6%
3.9843
 
< 0.1%
3.996350
 
7.2%
4.9920434
23.2%
5.9847
 
0.1%
5.997181
 
8.2%
ValueCountFrequency (%)
11.9949
 
0.1%
10.99549
 
0.6%
9.991302
 
1.5%
9.986
 
< 0.1%
8.992633
 
3.0%
8.977
 
< 0.1%
7.993654
4.2%
7.9832
 
< 0.1%
6.995929
6.7%
5.997181
8.2%
Distinct15776
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
Minimum2005-05-24 22:53:30
Maximum2006-02-14 15:16:03
Invalid dates0
Invalid dates (%)0.0%
2026-01-14T09:38:54.788915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:54.827279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

inventory_id
Real number (ℝ)

High correlation 

Distinct4567
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2296.5277
Minimum1
Maximum4581
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:54.864320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile213.95
Q11158
median2287
Q33428
95-th percentile4367
Maximum4581
Range4580
Interquartile range (IQR)2270

Descriptive statistics

Standard deviation1321.2774
Coefficient of variation (CV)0.575337
Kurtosis-1.1818466
Mean2296.5277
Median Absolute Deviation (MAD)1134
Skewness-0.0063442828
Sum2.0204851 × 108
Variance1745773.8
MonotonicityNot monotonic
2026-01-14T09:38:54.900679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112265
 
0.1%
112365
 
0.1%
15860
 
0.1%
233760
 
0.1%
409560
 
0.1%
242460
 
0.1%
237160
 
0.1%
189960
 
0.1%
242360
 
0.1%
296255
 
0.1%
Other values (4557)87375
99.3%
ValueCountFrequency (%)
130
< 0.1%
250
0.1%
320
 
< 0.1%
420
 
< 0.1%
650
0.1%
740
< 0.1%
820
 
< 0.1%
98
 
< 0.1%
1012
 
< 0.1%
118
 
< 0.1%
ValueCountFrequency (%)
458115
< 0.1%
45806
 
< 0.1%
457915
< 0.1%
45789
 
< 0.1%
457715
< 0.1%
457612
< 0.1%
457512
< 0.1%
45749
 
< 0.1%
457325
< 0.1%
457220
< 0.1%

store_id
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
1
47953 
2
40027 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters87980
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
147953
54.5%
240027
45.5%

Length

2026-01-14T09:38:54.933557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T09:38:54.953711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
147953
54.5%
240027
45.5%

Most occurring characters

ValueCountFrequency (%)
147953
54.5%
240027
45.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number87980
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
147953
54.5%
240027
45.5%

Most occurring scripts

ValueCountFrequency (%)
Common87980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
147953
54.5%
240027
45.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII87980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
147953
54.5%
240027
45.5%

manager_staff_id
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
1
47953 
2
40027 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters87980
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
147953
54.5%
240027
45.5%

Length

2026-01-14T09:38:54.977662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T09:38:54.997235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
147953
54.5%
240027
45.5%

Most occurring characters

ValueCountFrequency (%)
147953
54.5%
240027
45.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number87980
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
147953
54.5%
240027
45.5%

Most occurring scripts

ValueCountFrequency (%)
Common87980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
147953
54.5%
240027
45.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII87980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
147953
54.5%
240027
45.5%

staff_id
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
1
47953 
2
40027 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters87980
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
147953
54.5%
240027
45.5%

Length

2026-01-14T09:38:55.022046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T09:38:55.041508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
147953
54.5%
240027
45.5%

Most occurring characters

ValueCountFrequency (%)
147953
54.5%
240027
45.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number87980
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
147953
54.5%
240027
45.5%

Most occurring scripts

ValueCountFrequency (%)
Common87980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
147953
54.5%
240027
45.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII87980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
147953
54.5%
240027
45.5%

staff_fn
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
Mike
47953 
Jon
40027 

Length

Max length4
Median length4
Mean length3.5450443
Min length3

Characters and Unicode

Total characters311893
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMike
2nd rowMike
3rd rowMike
4th rowMike
5th rowMike

Common Values

ValueCountFrequency (%)
Mike47953
54.5%
Jon40027
45.5%

Length

2026-01-14T09:38:55.064716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T09:38:55.082832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mike47953
54.5%
jon40027
45.5%

Most occurring characters

ValueCountFrequency (%)
M47953
15.4%
i47953
15.4%
k47953
15.4%
e47953
15.4%
J40027
12.8%
o40027
12.8%
n40027
12.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter223913
71.8%
Uppercase Letter87980
 
28.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i47953
21.4%
k47953
21.4%
e47953
21.4%
o40027
17.9%
n40027
17.9%
Uppercase Letter
ValueCountFrequency (%)
M47953
54.5%
J40027
45.5%

Most occurring scripts

ValueCountFrequency (%)
Latin311893
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M47953
15.4%
i47953
15.4%
k47953
15.4%
e47953
15.4%
J40027
12.8%
o40027
12.8%
n40027
12.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII311893
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M47953
15.4%
i47953
15.4%
k47953
15.4%
e47953
15.4%
J40027
12.8%
o40027
12.8%
n40027
12.8%

staff_ln
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
Hillyer
47953 
Stephens
40027 

Length

Max length8
Median length7
Mean length7.4549557
Min length7

Characters and Unicode

Total characters655887
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHillyer
2nd rowHillyer
3rd rowHillyer
4th rowHillyer
5th rowHillyer

Common Values

ValueCountFrequency (%)
Hillyer47953
54.5%
Stephens40027
45.5%

Length

2026-01-14T09:38:55.105459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T09:38:55.124492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
hillyer47953
54.5%
stephens40027
45.5%

Most occurring characters

ValueCountFrequency (%)
e128007
19.5%
l95906
14.6%
H47953
 
7.3%
i47953
 
7.3%
y47953
 
7.3%
r47953
 
7.3%
S40027
 
6.1%
t40027
 
6.1%
p40027
 
6.1%
h40027
 
6.1%
Other values (2)80054
12.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter567907
86.6%
Uppercase Letter87980
 
13.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e128007
22.5%
l95906
16.9%
i47953
 
8.4%
y47953
 
8.4%
r47953
 
8.4%
t40027
 
7.0%
p40027
 
7.0%
h40027
 
7.0%
n40027
 
7.0%
s40027
 
7.0%
Uppercase Letter
ValueCountFrequency (%)
H47953
54.5%
S40027
45.5%

Most occurring scripts

ValueCountFrequency (%)
Latin655887
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e128007
19.5%
l95906
14.6%
H47953
 
7.3%
i47953
 
7.3%
y47953
 
7.3%
r47953
 
7.3%
S40027
 
6.1%
t40027
 
6.1%
p40027
 
6.1%
h40027
 
6.1%
Other values (2)80054
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII655887
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e128007
19.5%
l95906
14.6%
H47953
 
7.3%
i47953
 
7.3%
y47953
 
7.3%
r47953
 
7.3%
S40027
 
6.1%
t40027
 
6.1%
p40027
 
6.1%
h40027
 
6.1%
Other values (2)80054
12.2%

address_id
Real number (ℝ)

High correlation 

Distinct599
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean301.90181
Minimum5
Maximum605
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:55.150702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile33
Q1152
median301
Q3451
95-th percentile574
Maximum605
Range600
Interquartile range (IQR)299

Descriptive statistics

Standard deviation172.88345
Coefficient of variation (CV)0.57264794
Kurtosis-1.188535
Mean301.90181
Median Absolute Deviation (MAD)149
Skewness0.010253583
Sum26561321
Variance29888.687
MonotonicityNot monotonic
2026-01-14T09:38:55.187148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152272
 
0.3%
532251
 
0.3%
201244
 
0.3%
148239
 
0.3%
240233
 
0.3%
79233
 
0.3%
33223
 
0.3%
262221
 
0.3%
182220
 
0.3%
415216
 
0.2%
Other values (589)85628
97.3%
ValueCountFrequency (%)
5156
0.2%
6175
0.2%
7144
0.2%
8121
0.1%
9183
0.2%
10171
0.2%
11191
0.2%
12152
0.2%
13129
0.1%
14123
0.1%
ValueCountFrequency (%)
605102
0.1%
604102
0.1%
603144
0.2%
602158
0.2%
601141
0.2%
600141
0.2%
599129
0.1%
598145
0.2%
597120
0.1%
596134
0.2%

address
Text

Distinct599
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:55.305200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length38
Median length34
Mean length19.461798
Min length9

Characters and Unicode

Total characters1712249
Distinct characters66
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row758 Junan Lane
2nd row758 Junan Lane
3rd row758 Junan Lane
4th row758 Junan Lane
5th row76 Kermanshah Manor
ValueCountFrequency (%)
parkway11137
 
3.9%
manor9908
 
3.4%
avenue8963
 
3.1%
way8854
 
3.1%
lane8745
 
3.0%
street8622
 
3.0%
place8225
 
2.8%
loop8071
 
2.8%
boulevard7812
 
2.7%
drive7643
 
2.6%
Other values (957)201223
69.6%
2026-01-14T09:38:55.461157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
201223
 
11.8%
a178952
 
10.5%
e110192
 
6.4%
o87025
 
5.1%
r83504
 
4.9%
n82148
 
4.8%
167756
 
4.0%
i51591
 
3.0%
l51231
 
3.0%
u49041
 
2.9%
Other values (56)749586
43.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1004855
58.7%
Decimal Number299535
 
17.5%
Space Separator201223
 
11.8%
Uppercase Letter198759
 
11.6%
Dash Punctuation3499
 
0.2%
Open Punctuation2189
 
0.1%
Close Punctuation2189
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a178952
17.8%
e110192
11.0%
o87025
 
8.7%
r83504
 
8.3%
n82148
 
8.2%
i51591
 
5.1%
l51231
 
5.1%
u49041
 
4.9%
t41214
 
4.1%
y29485
 
2.9%
Other values (16)240472
23.9%
Uppercase Letter
ValueCountFrequency (%)
P26620
13.4%
L22014
11.1%
S21827
11.0%
B18491
9.3%
A17384
8.7%
M15293
 
7.7%
D10480
 
5.3%
W9464
 
4.8%
C9140
 
4.6%
T6523
 
3.3%
Other values (16)41523
20.9%
Decimal Number
ValueCountFrequency (%)
167756
22.6%
629479
9.8%
429025
9.7%
928334
9.5%
726587
 
8.9%
826234
 
8.8%
524905
 
8.3%
224523
 
8.2%
323339
 
7.8%
019353
 
6.5%
Space Separator
ValueCountFrequency (%)
201223
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3499
100.0%
Open Punctuation
ValueCountFrequency (%)
(2189
100.0%
Close Punctuation
ValueCountFrequency (%)
)2189
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1203614
70.3%
Common508635
29.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a178952
14.9%
e110192
 
9.2%
o87025
 
7.2%
r83504
 
6.9%
n82148
 
6.8%
i51591
 
4.3%
l51231
 
4.3%
u49041
 
4.1%
t41214
 
3.4%
y29485
 
2.4%
Other values (42)439231
36.5%
Common
ValueCountFrequency (%)
201223
39.6%
167756
 
13.3%
629479
 
5.8%
429025
 
5.7%
928334
 
5.6%
726587
 
5.2%
826234
 
5.2%
524905
 
4.9%
224523
 
4.8%
323339
 
4.6%
Other values (4)27230
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1712249
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
201223
 
11.8%
a178952
 
10.5%
e110192
 
6.4%
o87025
 
5.1%
r83504
 
4.9%
n82148
 
4.8%
167756
 
4.0%
i51591
 
3.0%
l51231
 
3.0%
u49041
 
2.9%
Other values (56)749586
43.8%

address2
Unsupported

Missing  Rejected  Unsupported 

Missing87980
Missing (%)100.0%
Memory size687.5 KiB

district
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
87980 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters87980
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
87980
100.0%

Length

2026-01-14T09:38:55.495655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T09:38:55.514899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
87980
100.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator87980
100.0%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
87980
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common87980
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
87980
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII87980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
87980
100.0%

postal_code
Real number (ℝ)

Distinct596
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50328.658
Minimum3
Maximum99865
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:55.539474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4855
Q125220
median50805
Q374750
95-th percentile95509
Maximum99865
Range99862
Interquartile range (IQR)49530

Descriptive statistics

Standard deviation28834.472
Coefficient of variation (CV)0.57292353
Kurtosis-1.1636062
Mean50328.658
Median Absolute Deviation (MAD)24669
Skewness-0.018899509
Sum4.4279153 × 109
Variance8.3142679 × 108
MonotonicityNot monotonic
2026-01-14T09:38:55.577313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52137299
 
0.3%
9668284
 
0.3%
92150272
 
0.3%
22474270
 
0.3%
31342251
 
0.3%
89459244
 
0.3%
30861239
 
0.3%
72394233
 
0.3%
18727233
 
0.3%
65750223
 
0.3%
Other values (586)85432
97.1%
ValueCountFrequency (%)
3122
0.1%
400138
0.2%
504130
0.1%
841108
0.1%
943133
0.2%
966118
0.1%
1027132
0.2%
1079122
0.1%
1195146
0.2%
1545190
0.2%
ValueCountFrequency (%)
99865126
0.1%
99780165
0.2%
99552156
0.2%
99457168
0.2%
99405178
0.2%
99124124
0.1%
98889141
0.2%
98883157
0.2%
98775121
0.1%
98573157
0.2%

city_id
Real number (ℝ)

Distinct597
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean301.38936
Minimum1
Maximum600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:55.690474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile31
Q1148
median304
Q3452
95-th percentile569
Maximum600
Range599
Interquartile range (IQR)304

Descriptive statistics

Standard deviation173.50817
Coefficient of variation (CV)0.57569442
Kurtosis-1.2104272
Mean301.38936
Median Absolute Deviation (MAD)152
Skewness-0.016232752
Sum26516236
Variance30105.086
MonotonicityNot monotonic
2026-01-14T09:38:55.725634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42275
 
0.3%
442272
 
0.3%
312271
 
0.3%
101251
 
0.3%
109244
 
0.3%
340239
 
0.3%
108233
 
0.3%
527233
 
0.3%
474223
 
0.3%
64221
 
0.3%
Other values (587)85518
97.2%
ValueCountFrequency (%)
1156
0.2%
2124
0.1%
3177
0.2%
4160
0.2%
5131
0.1%
6123
0.1%
7178
0.2%
8136
0.2%
9164
0.2%
10157
0.2%
ValueCountFrequency (%)
600131
0.1%
599171
0.2%
598157
0.2%
597138
0.2%
596153
0.2%
59581
0.1%
594123
0.1%
593144
0.2%
592139
0.2%
591137
0.2%

city
Text

Distinct597
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:55.811662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length21
Mean length8.3905433
Min length2

Characters and Unicode

Total characters738200
Distinct characters57
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowguas Lindas de Gois
2nd rowguas Lindas de Gois
3rd rowguas Lindas de Gois
4th rowguas Lindas de Gois
5th rowQomsheh
ValueCountFrequency (%)
de1920
 
1.8%
san946
 
0.9%
la727
 
0.7%
del581
 
0.5%
santa578
 
0.5%
el433
 
0.4%
hill411
 
0.4%
santiago395
 
0.4%
plata353
 
0.3%
felipe344
 
0.3%
Other values (673)100656
93.8%
2026-01-14T09:38:55.930025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a109594
 
14.8%
n52055
 
7.1%
o47619
 
6.5%
i46517
 
6.3%
e42688
 
5.8%
r38393
 
5.2%
u34216
 
4.6%
l30362
 
4.1%
s24830
 
3.4%
t24653
 
3.3%
Other values (47)287273
38.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter605191
82.0%
Uppercase Letter106585
 
14.4%
Space Separator19364
 
2.6%
Dash Punctuation3852
 
0.5%
Close Punctuation1522
 
0.2%
Open Punctuation1522
 
0.2%
Other Punctuation164
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a109594
18.1%
n52055
 
8.6%
o47619
 
7.9%
i46517
 
7.7%
e42688
 
7.1%
r38393
 
6.3%
u34216
 
5.7%
l30362
 
5.0%
s24830
 
4.1%
t24653
 
4.1%
Other values (16)154264
25.5%
Uppercase Letter
ValueCountFrequency (%)
S13282
 
12.5%
B9638
 
9.0%
A7252
 
6.8%
C7249
 
6.8%
P6320
 
5.9%
T6224
 
5.8%
K5920
 
5.6%
M5791
 
5.4%
L5210
 
4.9%
H4334
 
4.1%
Other values (16)35365
33.2%
Space Separator
ValueCountFrequency (%)
19364
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3852
100.0%
Close Punctuation
ValueCountFrequency (%)
)1522
100.0%
Open Punctuation
ValueCountFrequency (%)
(1522
100.0%
Other Punctuation
ValueCountFrequency (%)
/164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin711776
96.4%
Common26424
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a109594
15.4%
n52055
 
7.3%
o47619
 
6.7%
i46517
 
6.5%
e42688
 
6.0%
r38393
 
5.4%
u34216
 
4.8%
l30362
 
4.3%
s24830
 
3.5%
t24653
 
3.5%
Other values (42)260849
36.6%
Common
ValueCountFrequency (%)
19364
73.3%
-3852
 
14.6%
)1522
 
5.8%
(1522
 
5.8%
/164
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII738200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a109594
 
14.8%
n52055
 
7.1%
o47619
 
6.5%
i46517
 
6.3%
e42688
 
5.8%
r38393
 
5.2%
u34216
 
4.6%
l30362
 
4.1%
s24830
 
3.4%
t24653
 
3.3%
Other values (47)287273
38.9%

country_id
Real number (ℝ)

Distinct108
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.862946
Minimum1
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:55.967319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q129
median50
Q380
95-th percentile103
Maximum109
Range108
Interquartile range (IQR)51

Descriptive statistics

Standard deviation30.063209
Coefficient of variation (CV)0.52869595
Kurtosis-1.1599807
Mean56.862946
Median Absolute Deviation (MAD)27
Skewness0.056106303
Sum5002802
Variance903.79656
MonotonicityNot monotonic
2026-01-14T09:38:56.003329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
448637
 
9.8%
237724
 
8.8%
1035428
 
6.2%
504565
 
5.2%
604493
 
5.1%
154042
 
4.6%
803895
 
4.4%
753160
 
3.6%
972094
 
2.4%
452014
 
2.3%
Other values (98)41928
47.7%
ValueCountFrequency (%)
1109
 
0.1%
2457
 
0.5%
3111
 
0.1%
4301
 
0.3%
5195
 
0.2%
61884
2.1%
7150
 
0.2%
9403
 
0.5%
10302
 
0.3%
11134
 
0.2%
ValueCountFrequency (%)
109174
 
0.2%
108328
 
0.4%
107628
 
0.7%
106190
 
0.2%
105908
 
1.0%
104963
 
1.1%
1035428
6.2%
1021210
 
1.4%
101476
 
0.5%
100823
 
0.9%

country
Text

Distinct108
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:56.104856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length37
Median length29
Mean length8.1365765
Min length4

Characters and Unicode

Total characters715856
Distinct characters54
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrazil
2nd rowBrazil
3rd rowBrazil
4th rowBrazil
5th rowIran
ValueCountFrequency (%)
india8637
 
8.0%
china7724
 
7.2%
united7114
 
6.6%
states5428
 
5.0%
japan4565
 
4.2%
mexico4493
 
4.2%
brazil4042
 
3.8%
russian3895
 
3.6%
federation3895
 
3.6%
philippines3160
 
2.9%
Other values (122)54818
50.9%
2026-01-14T09:38:56.240351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a95628
 
13.4%
i80527
 
11.2%
n72671
 
10.2%
e58230
 
8.1%
t35504
 
5.0%
r31038
 
4.3%
d30203
 
4.2%
o26432
 
3.7%
s25093
 
3.5%
l20000
 
2.8%
Other values (44)240530
33.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter587642
82.1%
Uppercase Letter107199
 
15.0%
Space Separator19791
 
2.8%
Other Punctuation818
 
0.1%
Open Punctuation203
 
< 0.1%
Close Punctuation203
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a95628
16.3%
i80527
13.7%
n72671
12.4%
e58230
9.9%
t35504
 
6.0%
r31038
 
5.3%
d30203
 
5.1%
o26432
 
4.5%
s25093
 
4.3%
l20000
 
3.4%
Other values (16)112316
19.1%
Uppercase Letter
ValueCountFrequency (%)
I13962
13.0%
S11266
10.5%
C11076
10.3%
U8127
 
7.6%
A6693
 
6.2%
P6557
 
6.1%
M6538
 
6.1%
B5804
 
5.4%
R5699
 
5.3%
T5497
 
5.1%
Other values (13)25980
24.2%
Other Punctuation
ValueCountFrequency (%)
,438
53.5%
.380
46.5%
Space Separator
ValueCountFrequency (%)
19791
100.0%
Open Punctuation
ValueCountFrequency (%)
(203
100.0%
Close Punctuation
ValueCountFrequency (%)
)203
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin694841
97.1%
Common21015
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a95628
13.8%
i80527
 
11.6%
n72671
 
10.5%
e58230
 
8.4%
t35504
 
5.1%
r31038
 
4.5%
d30203
 
4.3%
o26432
 
3.8%
s25093
 
3.6%
l20000
 
2.9%
Other values (39)219515
31.6%
Common
ValueCountFrequency (%)
19791
94.2%
,438
 
2.1%
.380
 
1.8%
(203
 
1.0%
)203
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII715856
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a95628
 
13.4%
i80527
 
11.2%
n72671
 
10.2%
e58230
 
8.1%
t35504
 
5.0%
r31038
 
4.3%
d30203
 
4.2%
o26432
 
3.7%
s25093
 
3.5%
l20000
 
2.8%
Other values (44)240530
33.6%

category_id
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3390657
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:56.268530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q313
95-th percentile16
Maximum16
Range15
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.7002421
Coefficient of variation (CV)0.56364133
Kurtosis-1.2488883
Mean8.3390657
Median Absolute Deviation (MAD)4
Skewness0.039042849
Sum733671
Variance22.092276
MonotonicityNot monotonic
2026-01-14T09:38:56.295922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
156936
 
7.9%
26600
 
7.5%
16307
 
7.2%
66188
 
7.0%
75929
 
6.7%
145743
 
6.5%
85615
 
6.4%
95559
 
6.3%
35510
 
6.3%
135192
 
5.9%
Other values (6)28401
32.3%
ValueCountFrequency (%)
16307
7.2%
26600
7.5%
35510
6.3%
45088
5.8%
54662
5.3%
66188
7.0%
75929
6.7%
85615
6.4%
95559
6.3%
104552
5.2%
ValueCountFrequency (%)
164668
5.3%
156936
7.9%
145743
6.5%
135192
5.9%
124503
5.1%
114928
5.6%
104552
5.2%
95559
6.3%
85615
6.4%
75929
6.7%

category
Categorical

High correlation 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
Sports
6936 
Animation
6600 
Action
6307 
Documentary
6188 
Drama
5929 
Other values (11)
56020 

Length

Max length11
Median length9
Mean length6.5334735
Min length3

Characters and Unicode

Total characters574815
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFamily
2nd rowFamily
3rd rowFamily
4th rowFamily
5th rowMusic

Common Values

ValueCountFrequency (%)
Sports6936
 
7.9%
Animation6600
 
7.5%
Action6307
 
7.2%
Documentary6188
 
7.0%
Drama5929
 
6.7%
Sci-Fi5743
 
6.5%
Family5615
 
6.4%
Foreign5559
 
6.3%
Children5510
 
6.3%
New5192
 
5.9%
Other values (6)28401
32.3%

Length

2026-01-14T09:38:56.327877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sports6936
 
7.9%
animation6600
 
7.5%
action6307
 
7.2%
documentary6188
 
7.0%
drama5929
 
6.7%
sci-fi5743
 
6.5%
family5615
 
6.4%
foreign5559
 
6.3%
children5510
 
6.3%
new5192
 
5.9%
Other values (6)28401
32.3%

Most occurring characters

ValueCountFrequency (%)
i57268
 
10.0%
r49574
 
8.6%
o46108
 
8.0%
a44569
 
7.8%
n36764
 
6.4%
e36331
 
6.3%
m33546
 
5.8%
s31255
 
5.4%
c27829
 
4.8%
t26031
 
4.5%
Other values (20)185540
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter475349
82.7%
Uppercase Letter93723
 
16.3%
Dash Punctuation5743
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i57268
12.0%
r49574
10.4%
o46108
9.7%
a44569
9.4%
n36764
7.7%
e36331
7.6%
m33546
 
7.1%
s31255
 
6.6%
c27829
 
5.9%
t26031
 
5.5%
Other values (9)86074
18.1%
Uppercase Letter
ValueCountFrequency (%)
F16917
18.0%
C15260
16.3%
A12907
13.8%
S12679
13.5%
D12117
12.9%
N5192
 
5.5%
H4928
 
5.3%
T4668
 
5.0%
G4552
 
4.9%
M4503
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
-5743
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin569072
99.0%
Common5743
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i57268
 
10.1%
r49574
 
8.7%
o46108
 
8.1%
a44569
 
7.8%
n36764
 
6.5%
e36331
 
6.4%
m33546
 
5.9%
s31255
 
5.5%
c27829
 
4.9%
t26031
 
4.6%
Other values (19)179797
31.6%
Common
ValueCountFrequency (%)
-5743
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII574815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i57268
 
10.0%
r49574
 
8.6%
o46108
 
8.0%
a44569
 
7.8%
n36764
 
6.4%
e36331
 
6.3%
m33546
 
5.8%
s31255
 
5.4%
c27829
 
4.8%
t26031
 
4.5%
Other values (20)185540
32.3%

language
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size687.5 KiB
English
87980 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters615860
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEnglish
2nd rowEnglish
3rd rowEnglish
4th rowEnglish
5th rowEnglish

Common Values

ValueCountFrequency (%)
English87980
100.0%

Length

2026-01-14T09:38:56.356877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T09:38:56.373564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
english87980
100.0%

Most occurring characters

ValueCountFrequency (%)
E87980
14.3%
n87980
14.3%
g87980
14.3%
l87980
14.3%
i87980
14.3%
s87980
14.3%
h87980
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter527880
85.7%
Uppercase Letter87980
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n87980
16.7%
g87980
16.7%
l87980
16.7%
i87980
16.7%
s87980
16.7%
h87980
16.7%
Uppercase Letter
ValueCountFrequency (%)
E87980
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin615860
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E87980
14.3%
n87980
14.3%
g87980
14.3%
l87980
14.3%
i87980
14.3%
s87980
14.3%
h87980
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII615860
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E87980
14.3%
n87980
14.3%
g87980
14.3%
l87980
14.3%
i87980
14.3%
s87980
14.3%
h87980
14.3%

film_category_id
Real number (ℝ)

High correlation 

Distinct955
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean502.11853
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:56.399088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile48
Q1255
median496
Q3752
95-th percentile952
Maximum1000
Range999
Interquartile range (IQR)497

Descriptive statistics

Standard deviation288.32439
Coefficient of variation (CV)0.57421581
Kurtosis-1.1870348
Mean502.11853
Median Absolute Deviation (MAD)247
Skewness-0.010455299
Sum44176388
Variance83130.956
MonotonicityNot monotonic
2026-01-14T09:38:56.435524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
489288
 
0.3%
892276
 
0.3%
880275
 
0.3%
249273
 
0.3%
649264
 
0.3%
301261
 
0.3%
764252
 
0.3%
418248
 
0.3%
369248
 
0.3%
414240
 
0.3%
Other values (945)85355
97.0%
ValueCountFrequency (%)
1230
0.3%
228
 
< 0.1%
360
 
0.1%
4115
0.1%
560
 
0.1%
6147
0.2%
775
 
0.1%
872
 
0.1%
9108
0.1%
10184
0.2%
ValueCountFrequency (%)
100093
0.1%
99985
0.1%
99854
 
0.1%
99730
 
< 0.1%
99635
 
< 0.1%
99523
 
< 0.1%
99478
0.1%
993180
0.2%
99256
 
0.1%
99164
 
0.1%

film_actor_id
Real number (ℝ)

High correlation 

Distinct200
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.9536
Minimum1
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size687.5 KiB
2026-01-14T09:38:56.470668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q152
median102
Q3149
95-th percentile191
Maximum200
Range199
Interquartile range (IQR)97

Descriptive statistics

Standard deviation56.900302
Coefficient of variation (CV)0.56362824
Kurtosis-1.1747542
Mean100.9536
Median Absolute Deviation (MAD)48
Skewness0.0020452118
Sum8881898
Variance3237.6443
MonotonicityNot monotonic
2026-01-14T09:38:56.508271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107753
 
0.9%
181678
 
0.8%
198674
 
0.8%
144654
 
0.7%
102640
 
0.7%
60612
 
0.7%
150611
 
0.7%
37605
 
0.7%
23604
 
0.7%
90599
 
0.7%
Other values (190)81550
92.7%
ValueCountFrequency (%)
1305
0.3%
2387
0.4%
3311
0.4%
4274
0.3%
5496
0.6%
6279
0.3%
7479
0.5%
8317
0.4%
9381
0.4%
10362
0.4%
ValueCountFrequency (%)
200346
0.4%
199255
 
0.3%
198674
0.8%
197553
0.6%
196450
0.5%
195450
0.5%
194380
0.4%
193444
0.5%
192458
0.5%
191523
0.6%

Interactions

2026-01-14T09:38:50.409099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:41.782817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:42.535166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:43.083935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:43.655405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:44.293787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:44.825402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:45.384784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:46.026143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:46.552829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:47.067589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:47.677937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:48.217659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:48.747552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:49.262088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:49.885481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:50.443135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:41.854260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:42.568083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:43.117679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:43.689673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:44.326011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:44.858984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:45.420481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:46.057697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:46.583050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:47.099549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:47.709717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:48.249867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:48.779098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:49.374684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:49.916299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:50.479070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:41.902876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:42.601158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:43.154056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:43.725493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:44.359365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:44.894566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:45.455340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:46.090022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:46.615373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:47.134290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:47.743525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:48.282400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:48.810864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:49.408799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:49.949228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:50.517140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:41.949903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:42.638313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:43.191001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:43.762633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:44.395143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:44.933616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:45.492711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:46.124998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:46.648934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:47.169985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:47.779174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:48.318297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:48.845673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:49.443719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:49.983225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:50.551785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:41.986915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:42.675082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:43.228444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:43.798705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:44.428707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:44.969335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:45.529253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:46.157899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:46.683691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:47.203361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:47.813852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:48.351599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:48.879278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:49.478516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:50.017522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:50.586596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:42.026939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:42.708820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:43.262819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:43.833707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:44.461288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:45.004513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:45.565013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:46.191922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:46.714876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:47.236414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:47.847403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:48.385056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:48.910833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:49.513842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:50.049985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:50.622895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:42.066707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:42.747180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:43.300405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:43.870672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:44.496152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:45.039006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:45.676640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:46.225974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:46.749415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:47.270745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-14T09:38:48.420171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:48.945135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:49.549332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-14T09:38:48.455640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:48.978162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:49.584555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:50.118987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:50.694918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-14T09:38:42.815640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-14T09:38:44.564045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-14T09:38:45.746059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:46.292331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:46.814987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:47.338623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:47.954143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:48.487299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:49.009748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-14T09:38:50.151158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:50.729336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:42.175350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:42.847424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:43.408240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:44.053347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:44.596205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:45.141291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:45.779581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:46.324014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:46.844302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:47.370262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:47.986683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:48.519401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:49.039658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:49.650020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:50.181323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:50.764039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:42.216765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:42.881520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:43.443437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:44.088076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:44.627942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:45.175572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:45.815268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-14T09:38:47.644455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:48.182215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:48.713956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:49.229141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:49.848946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T09:38:50.374764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-14T09:38:56.547255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
activeactor_idaddress_idamountcategorycategory_idcity_idcountry_idcustomer_idfilm_actor_idfilm_category_idfilm_idinventory_idlengthmanager_staff_idpayment_idpostal_coderatingrental_durationrental_idrental_ratereplacement_coststaff_fnstaff_idstaff_lnstore_id
active1.0000.0000.0850.0270.0320.0210.1090.0940.0920.0000.0230.0230.0210.0290.0030.0920.1380.0140.0120.0210.0070.0180.0030.0030.0030.003
actor_id0.0001.0000.001-0.0070.0560.0110.0020.0010.0011.0000.0150.0150.0150.0150.0000.0010.0040.0430.041-0.0030.042-0.0010.0000.0000.0000.000
address_id0.0850.0011.0000.0140.0320.0240.042-0.0421.0000.001-0.003-0.003-0.003-0.0130.0751.000-0.0430.0190.028-0.0040.027-0.0220.0750.0750.0750.075
amount0.027-0.0070.0141.0000.0650.0870.0010.0130.014-0.0070.0270.0270.0270.0030.0160.014-0.0120.0340.1710.0020.706-0.0300.0160.0160.0160.016
category0.0320.0560.0320.0651.0001.0000.0330.0290.0320.0560.1390.1390.1380.1490.0300.0310.0290.1560.1610.0240.1450.1410.0300.0300.0300.030
category_id0.0210.0110.0240.0871.0001.0000.001-0.0030.0240.011-0.027-0.027-0.0270.0930.0190.024-0.0050.1130.1040.0080.116-0.0330.0190.0190.0190.019
city_id0.1090.0020.0420.0010.0330.0011.000-0.0590.0420.0020.0090.0090.009-0.0020.1050.042-0.0710.0200.021-0.0050.026-0.0010.1050.1050.1050.105
country_id0.0940.001-0.0420.0130.029-0.003-0.0591.000-0.0420.0010.0170.0170.0170.0120.102-0.0420.0010.0230.021-0.0050.0200.0040.1020.1020.1020.102
customer_id0.0920.0011.0000.0140.0320.0240.042-0.0421.0000.001-0.003-0.003-0.003-0.0130.0741.000-0.0430.0180.028-0.0040.027-0.0220.0740.0740.0740.074
film_actor_id0.0001.0000.001-0.0070.0560.0110.0020.0010.0011.0000.0150.0150.0150.0150.0000.0010.0040.0430.041-0.0030.042-0.0010.0000.0000.0000.000
film_category_id0.0230.015-0.0030.0270.139-0.0270.0090.017-0.0030.0151.0001.0001.0000.0540.034-0.003-0.0090.1200.113-0.0020.112-0.0400.0340.0340.0340.034
film_id0.0230.015-0.0030.0270.139-0.0270.0090.017-0.0030.0151.0001.0001.0000.0540.034-0.003-0.0090.1200.113-0.0020.112-0.0400.0340.0340.0340.034
inventory_id0.0210.015-0.0030.0270.138-0.0270.0090.017-0.0030.0151.0001.0001.0000.0540.032-0.003-0.0090.1180.112-0.0020.116-0.0400.0320.0320.0320.032
length0.0290.015-0.0130.0030.1490.093-0.0020.012-0.0130.0150.0540.0540.0541.0000.016-0.013-0.0040.1010.0870.0010.1050.0140.0160.0160.0160.016
manager_staff_id0.0030.0000.0750.0160.0300.0190.1050.1020.0740.0000.0340.0340.0320.0161.0000.0720.0900.0150.0070.0280.0000.0271.0001.0001.0001.000
payment_id0.0920.0011.0000.0140.0310.0240.042-0.0421.0000.001-0.003-0.003-0.003-0.0130.0721.000-0.0430.0170.028-0.0020.025-0.0220.0720.0720.0720.072
postal_code0.1380.004-0.043-0.0120.029-0.005-0.0710.001-0.0430.004-0.009-0.009-0.009-0.0040.090-0.0431.0000.0170.0150.0060.026-0.0010.0900.0900.0900.090
rating0.0140.0430.0190.0340.1560.1130.0200.0230.0180.0430.1200.1200.1180.1010.0150.0170.0171.0000.0960.0200.0380.1120.0150.0150.0150.015
rental_duration0.0120.0410.0280.1710.1610.1040.0210.0210.0280.0410.1130.1130.1120.0870.0070.0280.0150.0961.0000.0210.0720.0830.0070.0070.0070.007
rental_id0.021-0.003-0.0040.0020.0240.008-0.005-0.005-0.004-0.003-0.002-0.002-0.0020.0010.028-0.0020.0060.0200.0211.0000.0240.0040.0280.0280.0280.028
rental_rate0.0070.0420.0270.7060.1450.1160.0260.0200.0270.0420.1120.1120.1160.1050.0000.0250.0260.0380.0720.0241.0000.1090.0000.0000.0000.000
replacement_cost0.018-0.001-0.022-0.0300.141-0.033-0.0010.004-0.022-0.001-0.040-0.040-0.0400.0140.027-0.022-0.0010.1120.0830.0040.1091.0000.0270.0270.0270.027
staff_fn0.0030.0000.0750.0160.0300.0190.1050.1020.0740.0000.0340.0340.0320.0161.0000.0720.0900.0150.0070.0280.0000.0271.0001.0001.0001.000
staff_id0.0030.0000.0750.0160.0300.0190.1050.1020.0740.0000.0340.0340.0320.0161.0000.0720.0900.0150.0070.0280.0000.0271.0001.0001.0001.000
staff_ln0.0030.0000.0750.0160.0300.0190.1050.1020.0740.0000.0340.0340.0320.0161.0000.0720.0900.0150.0070.0280.0000.0271.0001.0001.0001.000
store_id0.0030.0000.0750.0160.0300.0190.1050.1020.0740.0000.0340.0340.0320.0161.0000.0720.0900.0150.0070.0280.0000.0271.0001.0001.0001.000

Missing values

2026-01-14T09:38:51.126176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-14T09:38:51.347059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

film_idtitledescriptionrelease_yearlanguage_idrental_durationrental_ratelengthreplacement_costratingactor_idactor_fnactor_lncustomer_idcust_fncust_lnemailactiverental_idrental_datereturn_datepayment_idamountpayment_dateinventory_idstore_idmanager_staff_idstaff_idstaff_fnstaff_lnaddress_idaddressaddress2districtpostal_codecity_idcitycountry_idcountrycategory_idcategorylanguagefilm_category_idfilm_actor_id
080BLANKET BEVERLYA Emotional Documentary of a Student And a Girl who must Build a Boat in Nigeria2006172.9914821.99G16FREDCOSTNER130CHARLOTTEHUNTERCHARLOTTE.HUNTER@sakilacustomer.org112005-05-24 22:53:30.0002005-05-26 22:04:30.00035042.992005-05-24 22:53:30.000367111MikeHillyer134758 Junan LaneNaN82639190guas Lindas de Gois15Brazil8FamilyEnglish8016
180BLANKET BEVERLYA Emotional Documentary of a Student And a Girl who must Build a Boat in Nigeria2006172.9914821.99G173ALANDREYFUSS130CHARLOTTEHUNTERCHARLOTTE.HUNTER@sakilacustomer.org112005-05-24 22:53:30.0002005-05-26 22:04:30.00035042.992005-05-24 22:53:30.000367111MikeHillyer134758 Junan LaneNaN82639190guas Lindas de Gois15Brazil8FamilyEnglish80173
280BLANKET BEVERLYA Emotional Documentary of a Student And a Girl who must Build a Boat in Nigeria2006172.9914821.99G193BURTTEMPLE130CHARLOTTEHUNTERCHARLOTTE.HUNTER@sakilacustomer.org112005-05-24 22:53:30.0002005-05-26 22:04:30.00035042.992005-05-24 22:53:30.000367111MikeHillyer134758 Junan LaneNaN82639190guas Lindas de Gois15Brazil8FamilyEnglish80193
380BLANKET BEVERLYA Emotional Documentary of a Student And a Girl who must Build a Boat in Nigeria2006172.9914821.99G200THORATEMPLE130CHARLOTTEHUNTERCHARLOTTE.HUNTER@sakilacustomer.org112005-05-24 22:53:30.0002005-05-26 22:04:30.00035042.992005-05-24 22:53:30.000367111MikeHillyer134758 Junan LaneNaN82639190guas Lindas de Gois15Brazil8FamilyEnglish80200
4333FREAKY POCUSA Fast-Paced Documentary of a Pastry Chef And a Crocodile who must Chase a Squirrel in The Gulf of Mexico2006172.9912616.99R42TOMMIRANDA459TOMMYCOLLAZOTOMMY.COLLAZO@sakilacustomer.org122005-05-24 22:54:33.0002005-05-28 19:40:33.000123772.992005-05-24 22:54:33.0001525111MikeHillyer46476 Kermanshah ManorNaN23343423Qomsheh46Iran12MusicEnglish33342
5333FREAKY POCUSA Fast-Paced Documentary of a Pastry Chef And a Crocodile who must Chase a Squirrel in The Gulf of Mexico2006172.9912616.99R103MATTHEWLEIGH459TOMMYCOLLAZOTOMMY.COLLAZO@sakilacustomer.org122005-05-24 22:54:33.0002005-05-28 19:40:33.000123772.992005-05-24 22:54:33.0001525111MikeHillyer46476 Kermanshah ManorNaN23343423Qomsheh46Iran12MusicEnglish333103
6333FREAKY POCUSA Fast-Paced Documentary of a Pastry Chef And a Crocodile who must Chase a Squirrel in The Gulf of Mexico2006172.9912616.99R105SIDNEYCROWE459TOMMYCOLLAZOTOMMY.COLLAZO@sakilacustomer.org122005-05-24 22:54:33.0002005-05-28 19:40:33.000123772.992005-05-24 22:54:33.0001525111MikeHillyer46476 Kermanshah ManorNaN23343423Qomsheh46Iran12MusicEnglish333105
7333FREAKY POCUSA Fast-Paced Documentary of a Pastry Chef And a Crocodile who must Chase a Squirrel in The Gulf of Mexico2006172.9912616.99R127KEVINGARLAND459TOMMYCOLLAZOTOMMY.COLLAZO@sakilacustomer.org122005-05-24 22:54:33.0002005-05-28 19:40:33.000123772.992005-05-24 22:54:33.0001525111MikeHillyer46476 Kermanshah ManorNaN23343423Qomsheh46Iran12MusicEnglish333127
8333FREAKY POCUSA Fast-Paced Documentary of a Pastry Chef And a Crocodile who must Chase a Squirrel in The Gulf of Mexico2006172.9912616.99R147FAYWINSLET459TOMMYCOLLAZOTOMMY.COLLAZO@sakilacustomer.org122005-05-24 22:54:33.0002005-05-28 19:40:33.000123772.992005-05-24 22:54:33.0001525111MikeHillyer46476 Kermanshah ManorNaN23343423Qomsheh46Iran12MusicEnglish333147
9373GRADUATE LORDA Lacklusture Epistle of a Girl And a A Shark who must Meet a Mad Scientist in Ancient China2006172.9915614.99G139EWANGOODING408MANUELMURRELLMANUEL.MURRELL@sakilacustomer.org132005-05-24 23:03:39.0002005-06-01 22:12:39.000110323.992005-05-24 23:03:39.0001711111MikeHillyer413692 Amroha DriveNaN35575230Jaffna88Sri Lanka3ChildrenEnglish373139
film_idtitledescriptionrelease_yearlanguage_idrental_durationrental_ratelengthreplacement_costratingactor_idactor_fnactor_lncustomer_idcust_fncust_lnemailactiverental_idrental_datereturn_datepayment_idamountpayment_dateinventory_idstore_idmanager_staff_idstaff_idstaff_fnstaff_lnaddress_idaddressaddress2districtpostal_codecity_idcitycountry_idcountrycategory_idcategorylanguagefilm_category_idfilm_actor_id
87970452ILLUSION AMELIEA Emotional Epistle of a Boat And a Mad Scientist who must Outrace a Robot in An Abandoned Mine Shaft2006140.9912215.99R133RICHARDPENN114GRACEELLISGRACE.ELLIS@sakilacustomer.org1160472005-08-23 22:42:48.0002005-08-25 02:48:48.00030890.992005-08-23 22:42:48.0002088222JonStephens118442 Rae Bareli PlaceNaN24321148Duisburg38Germany9ForeignEnglish452133
87971452ILLUSION AMELIEA Emotional Epistle of a Boat And a Mad Scientist who must Outrace a Robot in An Abandoned Mine Shaft2006140.9912215.99R184HUMPHREYGARLAND114GRACEELLISGRACE.ELLIS@sakilacustomer.org1160472005-08-23 22:42:48.0002005-08-25 02:48:48.00030890.992005-08-23 22:42:48.0002088222JonStephens118442 Rae Bareli PlaceNaN24321148Duisburg38Germany9ForeignEnglish452184
87972439HUNCHBACK IMPOSSIBLEA Touching Yarn of a Frisbee And a Dentist who must Fight a Composer in Ancient Japan2006144.9915128.99PG-13106GROUCHODUNST103GLADYSHAMILTONGLADYS.HAMILTON@sakilacustomer.org1160482005-08-23 22:43:07.0002005-08-31 21:33:07.00027998.992005-08-23 22:43:07.0002019111MikeHillyer1071177 Jelets WayNaN3305220Ilorin69Nigeria7DramaEnglish439106
87973439HUNCHBACK IMPOSSIBLEA Touching Yarn of a Frisbee And a Dentist who must Fight a Composer in Ancient Japan2006144.9915128.99PG-13175WILLIAMHACKMAN103GLADYSHAMILTONGLADYS.HAMILTON@sakilacustomer.org1160482005-08-23 22:43:07.0002005-08-31 21:33:07.00027998.992005-08-23 22:43:07.0002019111MikeHillyer1071177 Jelets WayNaN3305220Ilorin69Nigeria7DramaEnglish439175
87974585MOB DUFFELA Unbelieveable Documentary of a Frisbee And a Boat who must Meet a Boy in The Canadian Rockies2006140.9910525.99G24CAMERONSTREEP393PHILIPCAUSEYPHILIP.CAUSEY@sakilacustomer.org1160492005-08-23 22:50:12.0002005-08-30 01:01:12.000106713.992005-08-23 22:50:12.0002666111MikeHillyer398954 Lapu-Lapu WayNaN8816278Korolev80Russian Federation7DramaEnglish58524
87975585MOB DUFFELA Unbelieveable Documentary of a Frisbee And a Boat who must Meet a Boy in The Canadian Rockies2006140.9910525.99G31SISSYSOBIESKI393PHILIPCAUSEYPHILIP.CAUSEY@sakilacustomer.org1160492005-08-23 22:50:12.0002005-08-30 01:01:12.000106713.992005-08-23 22:50:12.0002666111MikeHillyer398954 Lapu-Lapu WayNaN8816278Korolev80Russian Federation7DramaEnglish58531
87976585MOB DUFFELA Unbelieveable Documentary of a Frisbee And a Boat who must Meet a Boy in The Canadian Rockies2006140.9910525.99G32TIMHACKMAN393PHILIPCAUSEYPHILIP.CAUSEY@sakilacustomer.org1160492005-08-23 22:50:12.0002005-08-30 01:01:12.000106713.992005-08-23 22:50:12.0002666111MikeHillyer398954 Lapu-Lapu WayNaN8816278Korolev80Russian Federation7DramaEnglish58532
87977585MOB DUFFELA Unbelieveable Documentary of a Frisbee And a Boat who must Meet a Boy in The Canadian Rockies2006140.9910525.99G61CHRISTIANNEESON393PHILIPCAUSEYPHILIP.CAUSEY@sakilacustomer.org1160492005-08-23 22:50:12.0002005-08-30 01:01:12.000106713.992005-08-23 22:50:12.0002666111MikeHillyer398954 Lapu-Lapu WayNaN8816278Korolev80Russian Federation7DramaEnglish58561
87978585MOB DUFFELA Unbelieveable Documentary of a Frisbee And a Boat who must Meet a Boy in The Canadian Rockies2006140.9910525.99G103MATTHEWLEIGH393PHILIPCAUSEYPHILIP.CAUSEY@sakilacustomer.org1160492005-08-23 22:50:12.0002005-08-30 01:01:12.000106713.992005-08-23 22:50:12.0002666111MikeHillyer398954 Lapu-Lapu WayNaN8816278Korolev80Russian Federation7DramaEnglish585103
87979585MOB DUFFELA Unbelieveable Documentary of a Frisbee And a Boat who must Meet a Boy in The Canadian Rockies2006140.9910525.99G106GROUCHODUNST393PHILIPCAUSEYPHILIP.CAUSEY@sakilacustomer.org1160492005-08-23 22:50:12.0002005-08-30 01:01:12.000106713.992005-08-23 22:50:12.0002666111MikeHillyer398954 Lapu-Lapu WayNaN8816278Korolev80Russian Federation7DramaEnglish585106